Conversation: 02
19 Feb 2026 11:15h - 11:30h
Conversation: 02
Summary
The conversation featured ServiceNow President and CPO Amit Zaveri discussing why he views trust as the new infrastructure for enterprise AI [10][13-16]. He argued that without clear visibility, auditing and compliance, enterprises cannot reliably deploy AI in critical workflows [14-16].
Zaveri noted that the industry is still figuring out how to embed AI, but the first step is getting employees to accept its usefulness and see personal productivity gains [23-25][29-30]. ServiceNow responded by retraining staff and giving them hands-on access to AI tools, allowing workers to experience faster, more efficient task completion [29-33]. By automating repetitive “skull-crushing” tasks, employees gain time for higher-value work, which in turn builds confidence in the technology [35-38]. He described this as a step-wise cultural shift that has already lifted adoption across engineering, finance, support and go-to-market teams [42-43].
Addressing fears of job loss, Zaveri compared AI to previous disruptions such as cloud and web, emphasizing that the speed of change creates uncertainty but not inevitable layoffs [49-54]. ServiceNow’s AI business has actually expanded, enabling hiring, market expansion and reinvestment of savings into new areas [57-61]. He highlighted that removing mundane work frees staff for higher-margin activities, improving both top-line and bottom-line performance [62-66].
Security emerged as the biggest barrier to agentic AI adoption; early concerns about visibility and control limited uptake until ServiceNow introduced security profiles and a control tower [70-73][74-76]. The company’s acquisition of Vesa, which provides access graphs for non-human identities, ensures that AI agents operate only within authorized roles [80-86]. After these safeguards, the volume of customer-deployed agentic workflows jumped 55-fold, and adoption is now driven by clear ROI rather than experimentation [73][99-104].
Zaveri predicts AI will become a foundational layer of all enterprise software, with firms that fail to embed it losing competitive advantage [106-110]. He also warned that ongoing regulatory and security challenges, especially around physical AI in operational technology, will keep trust and risk management at the forefront [127-133]. Overall, the discussion concluded that combining cultural reskilling, robust security controls and measurable value is essential for sustainable enterprise AI deployment [15][42][104][127].
Keypoints
Major discussion points
– Trust is the foundational “infrastructure” for enterprise AI.
Zaveri stresses that without trust, safety, auditing, compliance and visibility, companies cannot rely on AI for critical workflows - the lack of these elements makes AI adoption untenable [13-16].
– Building trust through cultural change and employee reskilling.
ServiceNow’s strategy involves retraining staff, giving them hands-on access to AI tools, and eliminating repetitive “skull-crushing” tasks so workers see tangible productivity gains, which in turn fuels broader enterprise-level adoption [24-38].
– Security and identity management are essential for agentic AI.
Companies worry about visibility, vulnerabilities, and control; ServiceNow responded by embedding security controls, creating AI “control towers,” and acquiring Vesa to manage non-human identities and permissions, arguing that without such safeguards agentic AI will not be adopted [70-86].
– Adoption pace is becoming more measured and ROI-driven.
Early hype about rapid, universal deployment proved optimistic; now enterprises adopt AI more thoughtfully, first securing the platform, then piloting use-cases that demonstrate clear ROI before scaling [95-104].
– Future outlook: AI as a core layer of software and the rise of physical/OT AI.
Zaveri sees AI as inseparable from next-generation software, with vendors needing deep domain expertise to add context to foundation models, while new regulatory and operational-technology challenges (e.g., AI-driven robotics in factories) will shape the next wave [106-112][127-133].
Overall purpose / goal of the discussion
The conversation was designed to illuminate ServiceNow’s perspective on how enterprises can responsibly and effectively embed AI-particularly agentic AI-by establishing trust, reskilling workforces, securing implementations, and positioning AI as a foundational component of future software and operational technology.
Overall tone
The dialogue begins with a formal, explanatory tone as the moderator frames the “trust as infrastructure” premise. As Zaveri describes internal initiatives, the tone shifts to pragmatic optimism, highlighting concrete steps and successes. When addressing security and the hype around AI, the tone becomes cautionary yet confident, emphasizing the need for robust safeguards. The closing remarks adopt a forward-looking, visionary tone, acknowledging ongoing challenges while expressing confidence in AI’s strategic role. Throughout, the tone remains professional and constructive, with a gradual move from exploratory questioning to assertive, solution-focused statements.
Speakers
– Amit Zaveri
– Role/Title: President and Chief Product Officer, ServiceNow [S1]
– Area of Expertise: Enterprise software, AI integration, product strategy
– Speaker 1
– Role/Title: Event moderator / host (introduces and closes the session) [S3]
– Area of Expertise: Not specified
– Arjun Karpal
– Role/Title: Senior Tech Correspondent, CNBC [S1]
– Area of Expertise: Technology journalism, AI and enterprise technology
Additional speakers:
– None identified beyond the three listed above.
The interview opened with host Arjun Karpal introducing Amit Zaveri, President and Chief Product Officer of ServiceNow, and asking why “trust is the new infrastructure” for AI in the enterprise [10-11].
Zaveri stresses that without clear visibility, auditability, compliance and safety mechanisms, enterprises cannot rely on AI for mission-critical workflows [13-16].
Human-centred trust and cultural shift – ServiceNow first focused on getting employees to accept AI as useful and to understand its value, a mindset shift that acknowledges AI’s rapid impact on work [24-26]. The company then retrained staff and gave them hands-on access to AI tools, letting workers experience faster, more efficient task completion in their daily roles [29-33]. By automating “skull-crushing” repetitive work, employees free up time for higher-value activities, reinforcing confidence in the technology [35-38]. This cultural programme has driven noticeable AI adoption across engineering, finance, customer support and go-to-market teams [42-43].
Job-loss narrative and business growth – Zaveri compared AI-driven disruption to earlier waves such as cloud and the web, arguing that the technology itself does not inherently cause layoffs [50-54]. In ServiceNow’s experience the AI business has become a billion-dollar-plus unit, enabling new hires, entry into additional market segments, and reinvestment of efficiency savings into higher-margin activities [57-61][62-66].
Security as the gatekeeper – Early hesitancy stemmed from a lack of visibility, vulnerability controls and governance [70-73]. ServiceNow responded by building an AI “control tower” that provides end-to-end oversight and visibility, a catalyst that helped the volume of customer-deployed agentic AI (autonomous AI agents) workflows surge 55 × once the control tower was in place [73-76]. Recognising that AI agents act as non-human identities, ServiceNow acquired Vesa to add access-graph technology that enforces granular permissions and prevents unauthorised data access [80-86]. Zaveri emphasised that AI agents change roles “every second” based on requirements, so security and identity controls must be built into the product, not added later [87-89]. ServiceNow’s broader security business is also a “billion-dollar-plus” operation, underscoring the company’s commitment to robust protection [70-73].
Adoption pace and ROI loop – The initial expectation that agentic AI would proliferate instantly proved overly optimistic. Companies now adopt a more measured, ROI-driven approach, piloting a few well-secured use cases before scaling, which aligns with the industry view that visibility and control are prerequisites for large-scale rollout [95-101][102-104][68-69][71-77].
AI’s role relative to SaaS – Zaveri contended that AI will act as a synergistic layer rather than replace existing SaaS products. Only about 5-10 % of ServiceNow’s intellectual property derives from foundation models; the remaining 90 % comes from ServiceNow’s own context-building and domain-specific engineering [106-112][115-121]. He also highlighted a partner ecosystem that includes OpenAI, Anthropic, Mistral and Google-Gemini, reinforcing a collaborative approach to AI development [115-121].
Future outlook – Regulatory, privacy and security frameworks will continue to evolve, with every country now formulating AI-specific rules [127-129]. Zaveri flagged the emerging challenge of “physical AI” in operational technology-such as humanoid robots and droids in factories-and the need to secure these systems as part of broader enterprise processes [130-133].
In sum, the discussion identified three pillars for sustainable enterprise AI: embedding trust as foundational infrastructure, investing in employee reskilling and cultural change, and delivering built-in security and identity controls for autonomous AI agents. ServiceNow’s experience shows that when these elements are in place, adoption accelerates, ROI becomes evident, and AI serves as a value-adding layer rather than a disruptive replacement [13-16][42-43][104-106][127-129].
IT, the technology. Ladies and gentlemen, and now I have the privilege of inviting our last speaker for the day, Mr. Amit Zaveri, President and Chief Product Officer, ServiceNow. Mr. Zaveri has spent his career at the intersection of enterprise software and AI, most recently leading ServiceNow’s push to embed AI agents into every corner of enterprise workflow. His perspective on agentic AI what it actually delivers versus what it promises is grounded in millions of enterprise deployments. He’ll be in conversation with Arjun Karpal, CNBC’s Senior Tech Correspondent. Please welcome our guest and the moderator.
All right. Hello, everyone. Hi, thanks so much for joining us. And if you’re watching online, thank you so much. Amit, let’s just kick off. You’ve got this view that trust is the new infrastructure in this age of AI. Can you just unpack what that means?
Yeah. Thank you, Arjun. I think if you look at what’s going on in the AI space, there’s a huge amount of interest in terms of using it in enterprise use cases as well, right? And without understanding what it’ll do for you and having any idea of what it landed up implementing inside your system, it becomes very hard to really depend on it. So that’s why without trust and safety and understanding of what’s happening in your underlying environment, it becomes very hard to expect to use AI in a lot of these enterprise use cases because your companies will not be able to do any auditing, compliance, visibility, and you wouldn’t really be able to really not run business without any kind of understanding of what’s going on.
So trust has to be a big part of it.
And trust, I guess, in the enterprise sense of the word probably has lots of different definitions, right? We’re talking about trust amongst employees, for example, but also from the cyber perspective, from the security and safety perspective you were just mentioning there. So it’s worth digging into some of these. How do you design some of these? In the enterprise, let’s start with perhaps the human element. at this point, because there’s a lot of concern from people right now about potential job losses and the impact AI could have on their roles as well. So from the human perspective, how do you design trust within the organization?
Yeah, no, I think it’s still something which I think the industry is still trying to figure out, to be honest. The way to think through this one is, one, everybody has to accept that AI is useful, and there’s a lot of opportunity to embed that in terms of your day -to -day lives. Second, I think there is a reality that this thing is transforming how the world works, and it’s moving very fast. So once you understand the principles and the value of it, then you start building together in terms of what the cultural shifts need to be, how people need to work together, and how do you help them understand the value while keeping their jobs very important and be able to bring them into the conversation.
So there’s a huge amount of cultural shift inside the company, as well as being able to kind of educate. Everybody in terms of what it delivers for you. So what we’ve been doing at ServiceNow, for example, we’ve been retraining our employees and giving them access to a lot of the AI capabilities, making sure they get to see what it does for their day -to -day life at the employee productive level and see that, okay, you know what, I could do my job faster, better, more efficiently, and free up more of my time to do other things which I couldn’t get to. Second thing after that, once you get them re -skilled, is to really now take it to the enterprise level, not just an employee.
Like, now how do you improve your processes? And the processes which are cutting across multiple departments, can I make that thing work faster? Can I get a better understanding of how it operates? And can you land up freeing up a lot of the human painful work you used to do, right? A lot of the repetitive tasks, which we used to call a skull -crushing task, which is making it so difficult in the day -to -day life that they can’t really get anything done beyond that. So if you remove those barriers, people start trusting that, oh, you know what, this is helping me. It’s getting my job done better. And it’s also getting me more understanding of new technologies.
So, I’m going to go ahead and start talking about the technology that I’m using. And you start accepting that in your day -to -day work environment and take that to the next level because you start innovating. So it’s a step process, I would say. And what we have done today at ServiceNow, we’ve seen the adoption go up a lot, be it in engineering, be it in finance, be it in customer support, be it in go -to -market, because they started playing with these technologies and bringing it to the day -to -day work environment. And from there, they’ve been starting to now innovate and come up with new ideas to help make their jobs better and how you make the customer’s life better long -term.
Does that set them up then for success when, you know, inevitably we will see the changing nature of work? And also, we’ve already seen some companies, you know, make layoffs and blame it on AI, whether that’s true or not is another debate. But certainly, there will be changing nature of work and organizations are rethinking the workforce. So by doing the reskilling, is this… Setting employees up.
No, I think you’re right. I mean, with any technology transformation, there’s always worries about job losses. It’s nothing, this is not the first time a technology shift has created that anxiety. It has happened with the cloud when the cloud happened. It happened with the web when the transformation happened towards that. I think the difference here is the speed sometimes and the uncertainty of understanding what it does to you as an individual in some cases. But I think the worry, a lot of the news out there is saying because of AI, we reduce our staffing. I think some of them are just using that as an excuse I’ve seen so far. If you look at our business, our AI business has grown significantly.
And we have been able to add more people because we were able to expand our tap. We’ve been able to get into a lot more new segments of market because of the investment. We’ve been able to reinvest a lot of that money we save because of AI into a lot of new areas. And that is one thing which I think a lot of companies are starting to realize. You take out a lot of the mundane tasks and move into the high value tasks. You can increase your top. line. Sure, you help on the bottom line with AI. There is a lot of work you can now outsource to autonomous agents and agentic workflows instead of having to do it by humans.
But the humans are now able to do a lot of other things you couldn’t and get into a lot of new segments. Now, business has grown significantly because we’ve been able to now take that savings and invest into a lot of new areas.
You mentioned agentic there, and I’m glad you did because the other part of this trust equation is what you were mentioning earlier around safety and security as well. Well, given the excitement around agentic AI and how much businesses want to adopt this, is there enough focus being put right now on the vulnerabilities from a cyber perspective when it comes to agentic AI?
I would say that’s probably the biggest concern for companies when they think about AI and agentic, right? If you look at last year, early last year, when we used to go and talk about agentic workflows or AI, most of the companies were worried about not having any kind of visibility, worried about vulnerabilities, worried about security, worried about control. And once we started introducing them the capability of controlling some of this implementation and having security profiles around the AI implementation, a lot more companies started adopting AI. Late last year, I would say middle of last year and late last year, the volume of our agentic workflows being adopted by customers went up by 55 times, 55x. Because what happened was they started feeling comfortable that one, they have visibility into all the AI systems.
Second, they have ability to secure it because you don’t want to lose access to your data or get it accessed externally without any kind of permissions. And once you start giving them that comfort factor, they’re starting to see the benefit of taking agentic AI and implementing that into the businesses, be a workflow around case management, incident management, triaging, be able to resolve issues. And that is a very, very valuable things for them. But once you only can do that once you have the security part of it. So, for example, we’ve been. Investing aggressively in the security space, our security. business itself is a billion dollars plus, but we’ve been adding now for AI agents. The AI agents are changing roles every second you call them based on what requirements you have.
So how do you manage the permissions? How do you manage their identity? So we bought recently a company called Vesa, which does access graphs for non -human identities, which makes it much more valuable to our customers because now they know that those agents are guaranteed to not do something nefarious or they won’t have access to data you’re not allowed to have. And whenever you change the roles, they’re only getting to do things based on the roles. So it’s a very important part of it. And I think agentic AI and things like that will not be adopted if you don’t have a right kind of security technology as part of this implementation. It cannot be on the side.
It has to be part of the product.
There’s certain tech companies who will sort of talk up the capabilities of agentic AI right now and talk up how enterprises are adopting AI. But what do you think? I think it’s a very important part of it. I think it’s a very important part of it. From your perspective, has the adoption of AI from enterprises been faster, slower, or about right than you had anticipated?
I think there was a lot of expectation early last year. Everybody thought that agentic AI and AI agents will be proliferated across every enterprise. I thought that was probably a little more optimistic and unrealistic because there were a lot of technologies which are missing to really provide you a platform which guarantees everything before you go and adopt it. A lot of those things started happening, I think I would say middle last year, and now the volume of adoption has gone up. But it is probably more thoughtful than probably experimental the way it was before. A lot of people were experimenting with it, but they were not wanting to put it in production because of the security things you talked about, trust and safety and compliance.
Now with a lot of the things customers are seeing from vendors like us, where you’re providing AI control tower, for example, to make sure you have visibility and control, they’re feeling more comfortable. So the volume is starting to go up. Use cases are getting much more defined. And what I’ve seen so far is that once you implement one or two use cases, you start seeing ROI. then the next more use cases become very very fast so you have to make it easy to be adopted you have to provide the security and everything else around it and then get them to see the roi and once you get the roi i think the customers all feel that this is something valuable to them and it’s something they want to invest in
i mean can i get your take on a on a comment we had on cnbc this week i was speaking to the ceo of mistral ai in europe and it was around this conversation happening in financial markets right now around software yeah um and how much are these agentic ai systems going to do the job of software that enterprises currently pay for uh and these sas businesses and he said you know he believes that 50 roughly of current you know software being used by enterprises uh could shift to ai i just wanted to get your take on that given how how embedded you are in this industry
no i think that there’s there’s a lot of people who are in the industry who are in the industry who are in the industry a lot of this debate about what is ai going to do the software industry i think uh ai is going to be a synergetic part of Any software you’re going to build going forward, and it’s already happening now, has to be with AI mindset and AI as part of the platform and the foundation. The companies which are going to suffer are the companies who are not adopting AI fast enough. So any vendor who’s thinking about AI as a side thing or something which is coming later, I think it’ll be very difficult to really justify customers buying that product.
Companies like us and others who are starting to make, we have been doing that for a few years, where they’ve been making AI part of the foundation, part of the platform. We’re already accelerating that adoption because customers, once they value, second, I think they do believe that this is going to be a very competitive advantage to them as well. And so we see a lot of synergy. We do a lot of partnership with OpenAI, Anthropic. We work with Mistral. We work with Google and Gemini because I think there’s a synergy between what foundational models and AI technology provides. and all the things you have to do around it. That’s what software industry can do. So what we’re doing is we’re building on top of it, but it’s like 5 % to 10 % of IP comes from those models.
90 % comes from technology we build because you have to build a lot of context around enterprise use cases. You have to understand what it means. You have to understand why an exception happened, how you handle it. Models are basically telling them what to do, but they don’t know why. The why part, the context part, comes from technologies and software we build. And the companies who are going to do that much more
better, understand domain, understand expertise, and have a lot of experience, will win in this market. And that’s the difference, I think. I mean, we’ve got about a minute left. I just wanted to get your take on the future. If we were sat here
I think we still will be talking about security and risk, definitely, because there’s a lot of work still to be done. regulations. Every country is now thinking about what AI means to them, what kind of regulations they want to put in for privacy, security, other things like that. I think the other one which is starting to come up a lot is physical AI. So we’re doing a lot of work in OT, operational technology, because a lot of the shop factories are changing with physical AI, with humanoids and droids and things like that, because they are going to be the next generational way of manufacturing. So how do you now secure that? How do you bring that as part of the processes?
How you integrate that into your environment is going to be a critical discussion as well.
Fantastic. Amit, thanks for your insights. So incisive. I appreciate your time. Thank you so much. Round of applause for Amit Zaveri of ServiceCamp. Thank you, everyone.
Mr. Amit Zaveri, and thanks, Arjun Karpal, for moderating this conversation. Ladies and gentlemen, with this, we end.
“Host Arjun Karpal introduced Amit Zaveri, President and Chief Product Officer of ServiceNow.”
The transcript snippet shows Amit Zaveri speaking and thanks Arjun Kharpal for moderating, confirming the host-speaker relationship [S1].
“ServiceNow acquired Vesa to add access‑graph technology that enforces granular permissions.”
The knowledge base records ServiceNow’s recent AI acquisition as Moveworks for $2.85 billion, with no mention of a Vesa acquisition, indicating the reported claim is inaccurate [S94].
“In ServiceNow’s experience the AI business has become a billion‑dollar‑plus unit.”
ServiceNow’s $2.85 billion purchase of Moveworks demonstrates a multi-billion-dollar commitment to AI, providing context for the size of its AI business [S94].
“Enterprises need clear visibility, auditability, compliance and safety mechanisms to rely on AI for mission‑critical workflows.”
Other sessions stress that AI systems must be auditable and safety-focused, reinforcing the importance of those mechanisms [S78].
“ServiceNow first focused on getting employees to accept AI as useful, retraining staff and giving them hands‑on access to AI tools to experience faster, more efficient task completion.”
Industry discussions highlight the value of hands-on workforce training and the shift toward human-centred AI adoption, echoing the described cultural programme [S64] and the broader benefit of automating repetitive work [S86].
The discussion reveals a strong consensus that trust, security, and employee reskilling are the foundational pillars for successful enterprise AI deployment. Both Amit and Arjun stress that without visibility, auditability and robust security controls, adoption stalls, and that cultural change through up‑skilling is needed to allay job‑loss concerns. Expectations about rapid, hype‑driven adoption have been tempered by the reality of security‑driven rollout, leading to a more measured, ROI‑focused trajectory.
High consensus on the necessity of trust, security, and reskilling; moderate consensus on adoption pace; limited disagreement on the extent to which AI will replace existing SaaS products.
The conversation reveals three main areas of disagreement: (1) whether AI leads to job cuts or creates new roles, (2) how much of current enterprise software will be supplanted by AI, and (3) whether the industry’s current cyber‑security focus on agentic AI is adequate. While both speakers share common goals—building trust, ensuring security, and up‑skilling workers—their viewpoints diverge on the expected outcomes and the pathways to achieve those goals.
Moderate. The disagreements are substantive but not antagonistic; they reflect differing interpretations of AI’s impact rather than outright conflict. This suggests that policy and industry discussions will need to balance optimism about AI‑driven growth with realistic assessments of job displacement risks and the need for robust security frameworks.
The discussion’s trajectory was shaped by Amit Zaveri’s framing of trust as the essential infrastructure for AI, his concrete examples of how security controls unlock massive adoption, and his counter‑narratives to common AI anxieties about job loss and software displacement. Each of these insights acted as a pivot point, steering the conversation from abstract hype to practical, governance‑focused implementation, and ultimately expanding the dialogue to encompass future challenges in regulation and physical AI. Collectively, these comments deepened the analysis, introduced new dimensions (human reskilling, identity for AI agents, synergy with existing software), and guided the interview toward a nuanced view of AI’s role in enterprise transformation.
Disclaimer: This is not an official session record. DiploAI generates these resources from audiovisual recordings, and they are presented as-is, including potential errors. Due to logistical challenges, such as discrepancies in audio/video or transcripts, names may be misspelled. We strive for accuracy to the best of our ability.
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